There is potential confusion between fundamental analysis (FA) and value investing (VI) in stock analysis, although they are closely related. Generally, this potential confusion may stem from an over-reliance on specific ratios. For instance, while a stock with a low market to book value ratio (M/B) may appear undervalued within the framework of value investing, this low ratio might actually reflect deteriorating fundamentals (i.e., a value trap), an issue that comprehensive fundamental analysis would uncover. This confusion can occur in the reverse scenario. An investor may believe they are engaging in value investing, while in reality, they might be conducting FA without fully conducting to the core principles of VI. An investor performing fundamental analysis might be looking at all key metrics such as earnings, dividends, and cash flows, however if they are not explicitly focusing on undervaluation and the margin of safety then they are not truly engaging in value investing. Instead, they are simply performing a general analysis of the stock's fundamentals.

This article investigates whether there are signs of confusion in academic research regarding the distinction between the scope FA and VI. This objective is motivated by Walkshäusl who identified that the f-Score, originally developed as a VI indicator is frequently discussed within the framework of FA (Walkshäusl, 2020). F-score was explicitly formulated and introduced by Piotroski (Piotroski, 2000) as a VI tool. Walkshäusl found that three out of eight articles addressed the f-score as a value investing indicator i.e., (Walkshäusl, 2017)- (Piotroski & So, 2012). Meanwhile, five others examined it as an indicator of a firm's fundamental i.e., (Turtle & Wang, 2017)- (Hyde, 2018).

The potential confusion will be examined from the perspective of the utilization of f-score indicator which is developed by Piotroski (Piotroski, 2000). This article presents two arguments to support that this indicator can represent the discussion on this potential confusion. First, Piotroski is the most cited research paper on VI (Piotroski, 2000). Even though it is explicitly referred to as a value investing indicator, the role of f-score as a value investing indicator is potentially neglected (Walkshäusl, 2020). Second, indicator of f-score consists of nine integrated evaluations representing three key financial ratios in assessing a company’s fundamentals (i.e., profitability, liquidity, and operating efficiency). It is recommended for identifying portfolios of low M/B value stocks. This specific recommendation is also reinforced by the formulation of a similar indicator called the g-score, introduced by Mohanram to identify stock portfolios with higher returns on high M/B value stocks (Mohanram, 2005). We expect that this study will contribute to develop a clear framework that outlines the distinctions and overlaps between fundamental analysis and value investing, serving as a reference for future research and assisting educators in effectively teaching these concepts.

This article conducte bibliometric analysis to investigate potential confusion in academic research regarding the distinction between FA and VI. This mothode quantitatively analyze academic literature and publications. It can navigate researchers to evaluate the impact of articles, journals, authors, and institutions in a specific field by examining patterns in citation and publication data. Our bibliometric analysis followed the stages outline by Indarti et al as described in Figure 1 (Indarti et al., 2020). We limit the article mining only to Scopus-indexed articles, and only include journal articles, conference papers, book chapters, and books. All articles were analyzed using Vos- viewer software that can provide literature mapping based on a certain unit of analysis. We used keywords of both the author and index as a unit of analysis. The analysis of keyword is a type of co-occurrence analysis in Vos-viewer software. Co-occurrence analysis calculates the frequency of keywords used in articles. This calculation is the basis for estimating the relationship between keywords from all articles visuall

Table1 presents the results of the article mining process based on the established protocol. In the initial phase, 374 Scopus-indexed articles citing (Piotroski, 2000) were identified.2 After excluding papers that fell outside the relevant subject areas, the dataset was reduced to 288 articles. These articles² Searching was conducted on August 30, 2021 were then categorized by document type, year of publication, and Scopus rankings, with a focus on journal articles as depicted in Figure 2. The majority of the analyzed documents were journal articles (261 articles, or 90.63% as shown in Figure 2.1. Most of these journal articles are ranked in the first quartile according to Scopus rankings (146 or 55.94%) out of a total of 261 journal articles Figure 2.2. Figure 2.3 illustrates a steady increase in the number of articles citing Piotroski (Piotroski, 2000) from 2001 to 2021.

Figure 3.1 illustrates keyword frequency density, where higher frequency keywords are represented by red areas. Additionally, more frequently used keywords are displayed with larger font sizes. Figure 4 provides further details, revealing that FA is the most frequently used keyword, while VI ranks fourth. FA also has the strongest connections with other keywords, with a total link strength of 89. In comparison, VI ranks third in total link strength, with a value of 52.

Figure 3.1 and Figure 4 suggest that most articles citing Piotroski (Piotroski, 2000) focus more on FA than VI. However, these findings do not provide a clear comparison of how the f-score keywords relate to FA and VI. To explore this, we analyzed the relationship between f-score keywords and both FA and VI. We identified four variations of the f-score keyword: (1) fscore, (2) f-score, (3) Piotroski’s f-score, and (4) f_score, which we assumed to have equivalent meanings. The f_score variation was excluded from further interpretation due to its weak link strength and lack of observed connections to FA or VI.Figure 3.2 to Figure 3.4 present the relationships between the remaining three f-score variations (i.e., f-score, fscore, and Piotroski’s f-score) and the FA and VI keywords.

Figure 3.2 presents the direct relationships between the f-score keyword and both FA and VI. While f-score is directly linked to both keywords, FA and VI themselves do not share a direct relationship, indicating that they were not simultaneously used in the same article. This suggests that discussions of FA and VI in the context of the f-score indicator were addressed separately. Figure 3.3 illustrates the connections between fscore, FA, and VI, revealing that fscore is directly associated with FA but not with VI. On the other hand, Figure 3.4 shows that Piotroski’s f-score is connected to VI, but no direct relationship with FA is observed. The findings from Figure 3.3 and Figure 3.4are consistent with those of Figure 3.2, confirming that FA and VI are treated as distinct topics in the analysis of the f-score indicator.

We performed additional searches of research papers and reviewed articles published after Walkshäusl (Walkshäusl, 2020) that cite (Piotroski, 2000) and contain specific terms in their titles, keywords, or abstracts. Our article mining focused on Scopus-indexed papers that reference (Piotroski, 2000) and include particular keywords (i.e., f-score; fscore; f_score and Piotroski f-score) to ensure that all identified articles concentrate on the f-score indicator. These four keywords were identified through our mapping of 372 articles citing Piotroski (Piotroski, 2000) using VOSviewer. We identified nine articles, detailed in Table 2, which include the authors, sample characteristics, and research objectives. The majority of these studies investigate the f-score indicator as a measure of financial performance (Festa et al., 2021)–(Rossi et al., 2020), i.e., (Chaudhari & Ghorpade, 2020)– (Gimeno et al., 2020). Furthermore, two articles analyze the f-score indicator within the framework of value investing i.e., (Jun et al., 2021), (Pätäri et al., 2021).

The utilization of f-score as a measure of FA does not significantly deviate from its original purpose as a tool for VI. However, the differing concepts of FA and VI present the potential for varying expectations regarding return estimations. VI is a strategy aimed at identifying undervalued stocks due to mispricing (Graham & Dodd, 1934), meanwhile FA is generally understood as an investment approach that considers a company’s prospects, primarily derived from financial statements, without regard to the fairness of stock prices (Abarbanell & Bushee, 1998)

In this context, we argue, drawing on utility theory, that stock returns derived from using f-score as a measure of VI should exceed those obtained from FA. Utility theory elucidates the level of human satisfaction in economic activities, guiding decisions regarding the consumption of goods or services among various alternatives. In investment terms, utility can be quantified by return on investment (Nawrocki & Viole, 2014-06). Therefore, the application of utility theory to the VI strategy compared to FA can be illustrated through higher expectations of investment returns.

The concept of utility maximization posits that investors tend to gravitate towards stocks with lower prices, even when FA metrics are identical. Evaluating undervalued stocks necessitates accurate price assessments, while FA does not take into account the fairness of stock prices. Therefore, investing in undervalued stocks is likely to yield greater expected utility than investments based on fair stock prices.

This study addresses the potential confusion between fundamental analysis (FA) and value investing (VI) by examining the application of (Piotroski, 2000) f-score. Our bibliometric analysis reveals that although the f-score is often used within the context of FA, it is more accurately categorized as VI framework. VI focuses on identifying undervalued stocks, whereas FA tends to assess a company's financial performance without considering stock mispricing. In addition, we identified two theoretical groups underlying the development of value investing indicators. The first group assumes rational investor behavior, encompassing theories like the Efficient Market Hypothesis and Rational Expectations Theory. The second group, grounded in Behavioral Finance, suggests that investor behavior is influenced by irrational factors, leading to persistent market inefficiencies. These groups provide distinct frameworks for the use of value investing indicators. Our study further categorizes these indicators into Single Value Investing Indicators (SVII) and Combined Value Investing Indicators (CVII), building on the foundational works of (Graham & Dodd, 1934) and (Lakonishok et al., 1994). We identify that while the f-score is frequently used in FA contexts, it is more accurately categorized as a Combined Value Investing Indicator (CVII). The distinction between these categories contributes to a clearer understanding of how the f-score should be applied within value investing. Overall, this study refines the differentiation between FA and VI, providing valuable insights for future research and educational efforts. This study has several limitations. First, it focuses solely on Scopusindexed articles, which may result in the exclusion of relevant research from other databases. Second, the bibliometric analysis relies on specific keywords related to the f-score, potentially overlooking studies that use alternative terminology. Further research could broaden the dataset and test the empirical application of value investing indicators.